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基于问句语义图神经网络的中文问句生成SQL语句研究
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Abstract:
自然语言问句转为结构化查询语句(Text-to-SQL)是语义解析领域中热点研究之一，其目标是将自然语言问句转化为数据库可以理解且执行的结构化查询语句。现有研究大部分仅考虑数据库层面的关联信息，忽略了问句中的实体关系信息的重要性。为了提高模型捕捉问句中语义的有用信息，本文在IGSQL模型基础上，引入问句中实体之间的图网络信息，通过注意力机制来自动学习问句和数据库模式之间的关联。在Chase数据集上的实验结果表明，本文提出模型的完全匹配率达到46.2%。相比较于基线模型，完全匹配率提升了6.3%。
The conversion of natural language questions into structured query statements (Text to SQL) is one of the hot research topics in the field of semantic parsing, with the goal of transforming natural language questions into structured query statements that can be understood and executed by databases. Most existing research only considers relational information at the database level, ignoring the importance of entity relationship information in questions. In order to improve the model’s ability to capture useful semantic information in questions, this paper introduces graph network information between entities in questions based on the IGSQL model, and automatically learns the association between questions and database patterns through attention mechanisms. The experimental results on the Chase dataset show that the proposed model has an exact matching accuracy of 46.2%. Compared to the baseline model, the exact matching accuracy has increased by 6.3%.
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